Trained Without My Consent: Detecting Code Inclusion In Language Models Trained on Code

arXiv:2402.09299v1 Announce Type: cross
Abstract: Code auditing ensures that the developed code adheres to standards, regulations, and copyright protection by verifying that it does not contain code from protected sources. The recent advent of Large Language Models (LLMs) as coding assistants in the software development process poses new challenges for code auditing. The dataset for training these models is mainly collected from publicly available sources. This raises the issue of intellectual property infringement as developers' codes are already included in …

abstract arxiv assistants code coding consent copyright copyright protection cs.lg cs.se development inclusion language language models large language large language models llms process protection regulations software software development standards type